AI Threat Intelligence: From Defense to Boardroom Strategy

Nearly half of security leaders now use threat intelligence for strategic planning and investment decisions—not just for blocking bad IPs. That’s the headline shift in the 2025 State of Threat Intelligence research: threat intel is becoming a business input, not a security side project.

I like this trend, but I’m also skeptical of how many organizations can actually pull it off with their current tooling and workflows. The report points to the same friction I see everywhere: more data, more vendors, more dashboards… and not enough integration or operational capacity to turn intelligence into action.

This post is part of our AI in Cybersecurity series, and it’s written for the people who have to justify spend, make programs work across teams, and show outcomes. We’ll use the report’s numbers as a reality check—then map out how AI in threat intelligence helps you mature faster, integrate cleaner, and make better decisions with fewer humans in the loop.

What the 2025 report signals: threat intel is now a strategy input

Threat intelligence has moved from “help SOC analysts investigate alerts” to “help leaders decide where to invest.” The report found 43% of security decision-makers use threat intelligence to guide strategic investments and planning, making it one of the most common enterprise use cases.

That matters because strategy use cases have a higher bar:

• A SOC can tolerate some noise if analysts can triage it.
• A board or risk committee won’t tolerate ambiguity when money, vendors, and operational risk are on the line.

The real shift: from indicator feeds to decision systems

Traditional threat intel programs often look like collections of feeds, reports, and ad hoc requests. Strategy-grade threat intelligence looks different. It behaves more like a decision system:

• It ties threats to your business services, suppliers, and geographies.
• It produces prioritization (not just “interesting activity”).
• It shows why an action is recommended and what outcome to expect.

This is where AI becomes practical. Not “AI for the sake of AI,” but automation that reduces uncertainty and time-to-decision.

People also ask: “Can threat intel actually influence business investment?”

Yes—when it’s translated into business terms. If intelligence can reliably answer “What’s most likely to hit us next quarter?” and “What control reduces that risk fastest?” it becomes relevant to planning cycles, vendor renewals, and insurance conversations.

AI helps because it can continuously connect signals across disparate sources (internal telemetry, intel reports, vendor advisories, dark web chatter, vulnerability trends) and keep those connections current as conditions change.

Spending is rising—AI is how you keep ROI from collapsing

The report says 91% plan to increase threat intelligence spending in 2026. It also notes that 76% of enterprises invest $250k+ per year in external threat intelligence products (excluding services), and 14% spend more than $1M.

More spend isn’t automatically better security. If your program expands faster than your ability to operationalize it, you get:

• More alerts and reports nobody reads
• Duplicate vendor capabilities
• Intelligence that doesn’t map to detections, response playbooks, or risk decisions

Third-party breaches are forcing the issue

One stat in the report stands out: data breaches involving a third party doubled from 2024 to 2025. That single trend explains why threat intelligence is increasingly used for purchasing decisions and resource allocation.

Supplier risk is noisy, fast-moving, and hard to measure with annual questionnaires. It needs continuous monitoring and prioritization. AI is well-suited for that because the job is mostly:

• Extracting entities (vendors, products, subsidiaries)
• Correlating weak signals (mentions, leaked credentials, exploit chatter)
• Scoring and ranking what’s relevant to your environment

How to measure threat intelligence ROI (without hand-waving)

The report notes that most organizations measure ROI through speed and efficiency gains. That’s a good starting point, but it’s easy to game. A stronger approach is to track a mix of operational and business outcomes.

Here’s a simple ROI scorecard that works well in practice:

1. Time-to-triage: median time from intel arrival to “actionable/not actionable” decision
2. Time-to-control: median time from intel insight to a concrete control change (block, patch, policy, vendor action)
3. Coverage gained: number of detections/playbooks/control checks created from intel in a quarter
4. Exposure reduced: count of high-risk assets/suppliers brought below a defined risk threshold
5. Incident cost avoided: conservative estimate tied to prevented exploitation (use ranges, not a single magical number)

AI improves ROI when it compresses steps 1–3. If intelligence can’t become an action quickly, it isn’t intelligence—it’s a newsletter.

Maturity is improving, but integration is still the bottleneck

The report shows threat intelligence maturity is trending up: 49% of respondents consider their program “advanced.” Yet more than half still aren’t there, and almost half cite poor integration with existing security tools as a major challenge.

I’m firmly opinionated on this: most “integration problems” are actually data model problems.

Why integration breaks (even with plenty of APIs)

Many teams attempt to integrate threat intel by pushing indicators into tools (SIEM, SOAR, EDR). That’s necessary—but it’s not sufficient.

Integration breaks because:

• Intel arrives as narrative text, PDFs, tickets, and emails
• Entities don’t match (product names, subsidiaries, typo-ridden domains)
• Context gets lost (confidence, relevance, time window, targeting)
• Teams don’t agree on what “actionable” means

The AI advantage: unify intel into a common language

AI helps integration when it’s used to normalize and enrich intelligence into a consistent internal format:

• Entity resolution: “Acme Payments LLC” = “AcmePay” = acquired subsidiary name
• TTP mapping: extracting tactics/techniques from reports and aligning them to detection logic
• Relevance filtering: matching intel to your asset inventory, tech stack, identity providers, and critical vendors
• Deduplication: collapsing multiple vendor insights into one coherent storyline

If you want one “snippet-worthy” truth from this section, it’s this:

Threat intelligence maturity improves fastest when intel is treated like data engineering, not report reading.

Vendor consolidation is coming—use AI criteria to choose wisely

The report found 81% of respondents plan to consolidate threat intelligence vendors. That’s not just procurement tightening the belt. It’s an admission that too many tools create fragmentation and conflicting signals.

Consolidation goes wrong when teams choose based on brand or feed volume. It goes right when teams choose based on how well intel becomes action.

What to demand from threat intelligence platforms in 2026

If you’re consolidating, evaluate vendors against outcomes that matter in an AI-assisted SOC and risk program:

• Workflow fit: can intelligence trigger cases, playbooks, and control changes automatically?
• Explainability: can analysts and risk owners see why an item is prioritized?
• Integration depth: native support for SIEM/SOAR/EDR, ticketing, asset inventory, and identity systems
• Entity and context quality: confidence scoring, freshness, targeting metadata, and clear kill-chain context
• Model governance (for AI features): audit trails, human override, and guardrails against hallucinations

A strong stance: if a vendor’s “AI” features can’t show provenance and reasoning, they’ll create more risk than value.

People also ask: “Will AI replace threat intelligence analysts?”

No. But it will change what good analysts do all day.

AI should take the first pass at:

• summarizing large intel narratives
• extracting indicators and TTPs
• mapping intel to your environment
• proposing response actions

Analysts should spend their time on:

• validating high-impact judgments
• running adversary emulation and detection improvements
• aligning intelligence with business risk decisions
• communicating clearly to executives and partners

In other words: AI reduces toil; humans own accountability.

A practical blueprint: using AI to turn intel into action

Threat intelligence programs stall when the loop isn’t closed. You collect intel, maybe you brief it, maybe you tag it… and then nothing changes.

Here’s a workflow I’ve found actually sticks, especially for enterprises scaling their AI security operations.

Step 1: Define three “intel-to-action” lanes

Pick lanes that map to real operational levers:

1. Detection lane: intel that should create/modify detections, hunting queries, or correlation rules
2. Exposure lane: intel that should drive patching, configuration changes, or identity hardening
3. Third-party lane: intel that changes supplier posture, contract requirements, or monitoring intensity

This prevents the “everything is important” failure mode.

Step 2: Use AI to triage relevance, not to make final calls

Deploy AI for classification and prioritization:

• Is this relevant to our stack?
• Does it match known adversaries targeting our sector?
• Is there a credible exploit path for our exposed assets?

Then require a human sign-off for high-impact actions (blocking critical domains, disabling vendor access, pausing a rollout).

Step 3: Automate the handoffs (where integration usually fails)

Integration isn’t a one-time connector. It’s the set of handoffs between teams and tools. Automate these:

• Create tickets with extracted context and recommended actions
• Attach affected assets/suppliers automatically
• Trigger SOAR playbooks for repeatable containment steps
• Feed validated intel back into detections and watchlists with expiration logic

The expiration logic part matters. Old intel that never sunsets becomes self-inflicted noise.

Step 4: Report outcomes in business language

If threat intelligence is now strategic, the reporting has to change too.

Instead of “we processed 1,200 intel items,” report:

• “We reduced exposed attack surface on 37 internet-facing systems tied to active exploitation.”
• “We raised monitoring on 12 critical suppliers after breach signals; two required remediation within 30 days.”
• “We cut time from intel receipt to control change from 9 days to 36 hours.”

Those are boardroom sentences. They create budget confidence.

Where this goes next for AI in cybersecurity

The report’s biggest message is that threat intelligence is graduating into a strategic function. Spending is rising, maturity is improving, and vendor consolidation is accelerating. The friction point is integration—and that’s exactly where AI has the most immediate, tangible payoff.

If you’re planning 2026 budgets right now, I’d frame the decision like this: buy less intel, but turn more of it into action. AI makes that possible by normalizing messy inputs, correlating across sources, and automating workflows that humans can’t scale.

Want a simple gut-check for your program? Ask whether your threat intelligence can reliably produce one of these within a week: a new detection, a reduced exposure, or a supplier decision. If not, your next investment shouldn’t be “more feeds.” It should be better automation and integration.

Where are you seeing the biggest gap—intel relevance, integration into tools, or getting business leaders to act on the risk?